K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Edureka

1. k-means clustering www.edureka.co/data-science Edureka’s Data Science Certification Training

2. What Will You Learn Today? 1 3 2 Types of clustering Introduction to Machine Learning Cluster analysis 6 5 4 Introduction to k- means clustering How k-means clustering work? Demo in R: Netflix use-case www.edureka.co/data-science Edureka’s Data Science Certification Training

3. What is Machine learning? Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Learn Algorithm Perform Training Data Build Model Feedback www.edureka.co/data-science Edureka’s Data Science Certification Training

4. ML Use Case – Google self driving car  Google self driving car is a smart, driverless car.  It collects data from environment through sensors.  Takes decisions like when to speed up, when to speed down, when to overtake and when to turn. www.edureka.co/data-science Edureka’s Data Science Certification Training

5. Types of Machine Learning Feed the classifier with training data set and predefined labels. Supervised learning It will learn to categorize particular data under a specific label. Unsupervised learning When and where should I buy a house? House features Area crime rate Bedrooms Distance to HQ Area (in sq.ft) Locality www.edureka.co/data-science Edureka’s Data Science Certification Training

6. Types of Machine Learning An image of fruits is first fed into the system. Supervised learning The system identifies different fruits using features like color, size and it categorizes them. When a new fruit is shown, it analyses its features and puts it into the category having similar featured items. Unsupervised learning www.edureka.co/data-science Edureka’s Data Science Certification Training

7. Unsupervised Learning Cluster Analysis www.edureka.co/data-science Edureka’s Data Science Certification Training

8. What is Clustering? Clustering means grouping of objects based on the information found in the data describing the objects or their relationship.  The goal is that objects in one group should be similar to each other but different from objects in another group.  It deals with finding a structure in a collection of unlabeled data. Some Examples of clustering methods are:  K-means Clustering  Fuzzy/ C-means Clustering  Hierarchical Clustering www.edureka.co/data-science Edureka’s Data Science Certification Training

9. Clustering Use Cases Marketing Discovering distinct groups in customer databases, such as customers who make lot of long-distance calls. Insurance Marketing Insurance Identifying groups of crop insurance policy holders with a high average claim rate. Farmers crash crops, when it is “profitable”. Use-cases Land use Identification of areas of similar land use in a GIS database. Land use Seismic studies Seismic studies Identifying probable areas for oil/gas exploration based on seismic data www.edureka.co/data-science Edureka’s Data Science Certification Training

10. Types of clustering www.edureka.co/data-science Edureka’s Data Science Certification Training

11. Types of Clustering Exclusive Clustering Hierarchial Clustering Overlapping Clustering • An item belongs exclusively to one cluster, not several. • K-means does this sort of exclusive clustering. • When two cluster have a parent- child relationship or a tree-like structure then it is Hierarchical clustering • An item can belong to multiple clusters • Its degree of association with each cluster is known • Fuzzy/ C-means does this sort of exclusive clustering. Cluster 0 Cluster 1 Cluster 2 Cluster 1 Cluster 2 www.edureka.co/data-science Edureka’s Data Science Certification Training

12. K-means clustering www.edureka.co/data-science Edureka’s Data Science Certification Training

13. K-means clustering k-means clustering k-means clustering is one of the simplest algorithms which uses unsupervised learning method to solve known clustering issues. Total population Divides entire dataset into k clusters. k-means clustering require following two inputs. 1. K = number of clusters 2. Training set(m) = {x1, x2, x3,......, xm} Group 1 Group 2 Group 3 Group 4 www.edureka.co/data-science Edureka’s Data Science Certification Training

14. Example - Google News Various news URLs related to Trump and Modi are grouped under one section. K-means clustering automatically clusters new stories about the same topic into pre-defined clusters. www.edureka.co/data-science Edureka’s Data Science Certification Training

15. Example The plot of students in an area is as given below, I need to find specific locations to build schools in this area so that the students doesn’t have to travel much www.edureka.co/data-science Edureka’s Data Science Certification Training

16. Example - Solution This looks good www.edureka.co/data-science Edureka’s Data Science Certification Training

17. But how did he do that?... I’ll show you how www.edureka.co/data-science Edureka’s Data Science Certification Training

18. How k-means work? www.edureka.co/data-science Edureka’s Data Science Certification Training

19. How k-means work? The WSS is defined as the sum of the squared distance between each member of the cluster and its centroid. Choose number of clusters Mathematically: Initialization Cluster assignment where, p(i)= data point Move centroid q(i)= closest centroid to data point Optimization The idea of the elbow method is to choose the k after which the WSS decrease is almost constant. Convergence www.edureka.co/data-science Edureka’s Data Science Certification Training

20. How k-means work? Randomly initialize k points called the cluster centroids. Here, k = 2 Choose number of clusters Value of k(number of clusters) can be determined by the elbow curve. Initialization Cluster assignment Y-axis Move centroid Optimization Cluster centroid Convergence X-axis www.edureka.co/data-science Edureka’s Data Science Certification Training

21. How k-means work? Compute the distance between the data points and the cluster centroid initialized.  Choose number of clusters Depending upon the minimum distance, data points are divided into two groups.  Initialization Cluster assignment Move centroid Optimization Convergence www.edureka.co/data-science Edureka’s Data Science Certification Training

22. How k-means work? Compute the mean of blue dots.  Choose number of clusters Reposition blue cluster centroid to this mean.  Compute the mean of orange dots.  Initialization Reposition orange cluster centroid to this mean.  Cluster assignment Move centroid Optimization Convergence www.edureka.co/data-science Edureka’s Data Science Certification Training

23. How k-means work? Repeat previous two steps iteratively till the cluster centroids stop changing their positions. Choose number of clusters Initialization Cluster assignment Move centroid Optimization Convergence www.edureka.co/data-science Edureka’s Data Science Certification Training

24. How k-means work? Finally, k-means clustering algorithm converges.  Choose number of clusters Divides the data points into two clusters clearly visible in orange and blue.  Initialization Cluster assignment Move centroid Optimization Convergence www.edureka.co/data-science Edureka’s Data Science Certification Training

25. Problem Statement Challenge: Netflix wanted to increase its business by showing most popular movies on its website. Solution: So, Netflix decided to group the movies based on budget, gross and facebook likes Approach: For this, Netflix took imdb dataset of 5000 values and applied k-means clustering to group it. But how would I know which movie set to show and which to not ? www.edureka.co/data-science Edureka’s Data Science Certification Training

26. Demo www.edureka.co/data-science Edureka’s Data Science Certification Training

27. Solution – R Script www.edureka.co/data-science Edureka’s Data Science Certification Training

28. Output  We got three clusters based on budget and gross.  Lets see how good are these clusters.  Using command cl gives following output. Within cluster sum of squares by cluster: (between_SS / total _ SS = 72.4 %)  Higher the %age value, better is the model. www.edureka.co/data-science Edureka’s Data Science Certification Training

29. Output Further, lets relate cluster assignment to individual characteristics like director facebook likes(column 5) and movie facebook likes(column 28). Cluster 2 has maximum movie likes as well as director likes. www.edureka.co/data-science Edureka’s Data Science Certification Training

30. I want to know the profit values of movie Try this out www.edureka.co/data-science Edureka’s Data Science Certification Training

31. Hmm… I will go with cluster 2. It is making maximum profit and has maximum facebook likes. www.edureka.co/data-science Edureka’s Data Science Certification Training

32. Course Details Get Edureka Certified in Data Science Today! Go to www.edureka.co/data-science Gnana Sekhar says - “Edureka Data science course provided me a very good mixture of theoretical and practical training. LMS pre recorded sessions and assignments were very good as there is a lot of information in them that will help me in my job. Edureka is my teaching GURU now...Thanks EDUREKA.” Shravan Reddy says- “I would like to recommend any one who wants to be a Data Scientist just one place: Edureka. Explanations are clean, clear, easy to understand. Their support team works very well.. I took the Data Science course and I'm going to take Machine Learning with Mahout and then Big Data and Hadoop”. Balu Samaga says - “It was a great experience to undergo and get certified in the Data Science course from Edureka. Quality of the training materials, assignments, project, support infrastructures are a top notch.” What our learners have to say about us! and other www.edureka.co/data-science Edureka’s Data Science Certification Training

33. www.edureka.co/data-science Edureka’s Data Science Certification Training